When I was learning science in high school, I was mesmerized by the notion that scientific facts were true, myths were false, and there were still things that needed to be „figured out“. I was very impressed by the way computers were all about 1’s and 0’s (it wasn’t until much later that I learned computers didn’t actually divide truth and falsehood quite that neatly). Several years ago, I made a graphic image that shows the difference between the way it appears that humans think vs. the way it appears that computers think.

Note that I didn’t label which side represents human thinking vs. computer thinking. What we usually experience when we use computers is either TRUE or FALSE – we are not normally aware that there is actually a „DON’T KNOW“ state in between those two extremes. About a decade ago, I was very adamant about three-state logics.

Several decades ago, when I was just embarking on dissertation research (which was never finished, but that story is beyond the scope of this article), I was very adamant about something called „modal logic“ – a field in philosophy (and linguistics) which focuses on human modes of thought (such as „knowing“ vs. „believing“). Since humans often make references to such modes, I was hoping to unlock a hidden treasure behind such concepts. Yet they remain elusive to me to this day, even though I may quite often be heard to utter something like „I think…“ or „I believe…“ or indeed many such modes (usually using so-called „modal verbs“).

I think the less room we allow for such modalities – the smaller the amount of space we make for cases in which we acknowledge that we really don’t know, the more likely we are to make mistakes / errors.

Statisticians might be very cool to acknowledge „type 1“ and „type 2“ errors without even batting an eyelash, but for most regular folks it makes a world of difference whether we want X, whether we fear Y, whether we hope or wish or whatever.

Such very human modes of thought are rampant in our everyday lives and thinking, yet they are not given very much (or even any) room in the computer world. When there is no room whatsoever for „maybe“, then I predict the algorithms processing the data will probably be wrong.

There is a spectre haunting the Web: That spectre is populism.

Let me backtrack a moment. This piece is a part of an ongoing series of posts about „rational media“ – a concept that is still not completely hard and fast. I have a hunch that the notion of „trust“ is going to play a central role… and trust itself is also an extremely complex issue. In many developed societies, trust is at least in part based on socially sanctioned institutions (cf. e.g. „The Social Construction of Reality“) – for example: public education, institutions for higher education, academia, etc. Such institutions permeate all of society – be it a traffic sign at the side of a road, or a crucifix as a central focal element on the alter in a church, or even the shoes people buy and walk around with on a daily basis.

The Web has significantly affected the role many such institutions play in our daily lives. For example: one single web site (i.e. the information resources available at a web location) may be more trusted today than an encyclopedia produced by thousands of writers ever were – whether centuries ago, decades ago, or even still just a few years past.

Similarly, another web site may very well be trusted by a majority of the population to answer any and all questions whatsoever – whether of encyclopedic nature or not. Perhaps such a web site might use algorithms – basically formulas – to arrive at a score for the „information value“ of a particular web page (the HTML encoded at one sub-location of a particular web site). A large part of this formula might involve a kind of „voting“ performed anonymously – each vote might be no more than a scratch mark presumed to indicate a sign of approval (an „approval rating“) given from disparate, unknown sources. Perhaps a company might develop more advanced methods in order to help guage whether the vote is reliable or whether it is suspect (for example: one such method is commonly referred to as a „nofollow tag“ – a marker indicating that the vote should not be trusted).

What many such algorithms have in common is that on a very basic level, they usually rely quite heavily on some sort of voting mechanism. This means they are fundamentally oriented towards populism – the most popular opinion is usually viewed as the most valid point of view. This approach is very much at odds with logic, the scientific method and other methods that have traditionally (for several centuries, at least) be used in academic institutions and similar „research“ settings. At their core, such populist algorithms are not „computational“ – since they rely not on any kind of technological solution to questions, but rather scan and tally up the views of a large number of human (and/or perhaps robotic) „users“. While such populist approaches are heralded as technologically advanced, they are actually – on a fundamental level – very simplistic. While I might employ such methods to decide which color of sugar-coated chocolate to eat, I doubt very much that I, personally, would rely on such methods to make more important – for example: „medical“ – decisions (such as whether or not to undergo surgery). I, personally, would not rely on such populist methods much more than I would rely on chance. As an example of the kind of errors that might arise from employing such populist methods, consider the rather simple and straightforward case that some of the people voting could in fact be color-blind.

Yet that is just the beginning. Many more problems lurk under the surface, beyond the grasp of merely superficial thinkers. Take, for example, the so-called „bandwagon effect“ – namely, that many people are prone to fall into a sort of „follow the leader“ kind of „groupthink“. Similarly, it is quite plausible that such bandwagon effects could even influence not only people’s answers, but even also the kinds of questions they feel comfortable asking (see also my previous post). On a more advanced level, complex systems may be also be influenced by the elements they comprise. For example: While originally citation indexes were designed with the assumption that such citation data ought to be reliable, over the years it was demonstrated that such citations are indeed very prone to be corrupted by a wide variety of corruption errors and that citation analysis is indeed not at all a reliable method. While citation data may have been somewhat reliable originally, it became clear that eventually citation fraud corrupted the system.

One of my favorite authors in the field of „search“ is John Battelle. Although he was not trained in the field of information science or information retrieval, his experience in the fields of journalism and publishing at the cusp of the so-called „information revolution“ apparently led him to learn many things sort of by osmosis.

One of my favorite ideas of his is the way he talks about human-computer interaction. Initially, this was almost exclusively text-based. Then, he notes, with the advent of „graphical user interfaces“ (GUIs), computers became more and more instruments with which humans, would point at stuff. He has presented this idea quite often, I don’t even know which presentation I should refer, link or point to – which one I should index.

In the early days of search, the book was ubiquitous. Indeed, several hundred years ago it almost seems as though each and every question could be answered with one single codex – and this codex was called „Bible“ (which means, essentially, „the books“). We have come a long way, baby. Today, we might say that online, the text box is king“ (Tom Paine, eat your heart out! ).

Although computer manufacturers desparately try to limit the choices consumers have once they have acquired their machines with loads of previously installed (and usually highly sponsored) software, it will not be very long before the typical consumer is confronted with a text box in order to interact with his or her mish-mash of hardware and software. Even without typing out any text whatsoever, whenever a human presses on a button to take a picture or clicks on an icon to record an audio or video, the associated files are given a text-string filename by the gizmo machinery. All of the code running on each and every machine is written out in plain text somewhere. When computers write their own Bible, it is quite probable that they would start off with something like „In the beginning was the text, and it was human.“

If humans ever asked an „artificially intelligent“ computer a question like „what is love?“ the computer would probably be very hard-pressed not to respond „a four-letter word“.

In our brains, almost everything is connected to the world outside of our brains. Thinking about artificial intelligence (AI), my friends Ted and Brandon are asking for help (@http://concerning.ai). In my humble opinion: If you want to „get somewhere“ then you need to think „outside of the box“.

What I’m writing here has mainly to do with things Brandon and Ted talk about in episode 10. Also, in episodes 11 and 12, Brandon and Ted talk with Evan Prodromou, a „practitioner“ in the field. Evan points out (at least) two fascinating points: 1. Procedural code and 2. Training sets. Below, I will also talk about these two issues.

When I said above that there is a need to „think out side of the box“, I was alluding to much larger systems than what is usually considered (note that Evan, Ted and Brandon also touched on a notion of „open systems“). For example: Language. So-called „natural language“ is extremely complex. To present just a shimmer of the enormous complexity of natural language, consider the „threshold anecdote“ Ted shared at the beginning of episode 11. A threshold is both a very concrete thing and also an abstract concept. When people use the term „threshold“, other people can only understand the meaning of the term by at the same time also considering the context in which the term is being used. This is for all practical purposes an intractable problem for any computational device which might be constructed by humans sometime in the coming century. Language itself does not exist in one person or one book, but it is something which is distributed among a large number of people belonging to the same linguistic community. The data is qualitative rather than qantitative. Only the most fantastically optimistic researchers would ever venture to try to „solve“ language computationally – and I myself was also once one such researcher. I doubt humans will ever be able to build such a machine… not only due to the vast resources it might require, but also because the nature of (human) natural language is orthogonal to the approach of „being solvable“ via procedural code.

Another anecdote I have often used to draw attention to how ridiculous the aim to „solve language“ seems is Kurzweil’s emphasis on pattern recognition. Patterns can only be recognized if they have been previously defined. Keeping with another example from episode 11, it would require humans to walk from tree to tree and say „this is an ash tree“ and „that is not an ash tree“ over and over until the computational device were able to recognize some kind of pattern. However, the pattern recognized might be something like „any tree located at a listing of locations where ash trees grow“. Indeed: The hope that increasing computational resources might make pattern recognition easier underscores the notion that such „brute force“ procedures might be applied. Yet the machine would nonetheless not actually understand the term „ash tree“. A computer can recognize what an ash tree is IFF (if and only if) a human first defines the term. If a human must first define the term, then there is in fact no „artificial intelligence“ happening at all.

I have a hunch that human intelligence has evolved according to entirely different laws – „laws of nature“ rather than „laws of computer science“ (and/or „mathematical logic“). Part of my thinking here is quite similar to what Tim Ferris has referred to as „not-to-do lists“ (see „The 9 Habits to Stop Now“). Similarly, it is well-known that Socrates referred to „divine signs“ which prevented him from taking one or another course of action. You might also consider (from the field of psychology) Kurt Lewin’s „Field Theory“ (in particular the “Force Field Analysis” of positive / negative forces) in this context, and/or (from the field of economics) the „random walk“ hypothesis. The basic idea is as follows: Our brains have evolved with a view towards being able to manage (or „deal with“) situations we have never experienced before. Hence „training sets“ are out of the question. We are required to make at best „educated“ guesses about what we should do in any moment. Language is a tool-set which has symbiotically evolved in our environment (much like the air we breathe is also conducive to our own survival). Moreover: Both we and our language (as also other aspects of our environment) continue to evolve. Taken to the ultimate extreme, this means that the coexistence of all things evolving in concert shapes the intelligence of each and every sub-system within the universe. To put it rather plainly: the evolution of birds and bees enables us to refer to them as birds and bees; the formation of rocks and stars enables us to refer to them as rocks and stars; and so on.

In case you find all of this somewhat scientific theory too theoretical, please feel free to check out one of my recently launched projects – in particular the „How to Fail“ page … over at bestopopular.com (which also utilizes the „negative thinking“ approach described above).